Abstract—In this paper we study the problem of protecting
privacy in the publication of transactional data. Consider a
collection of transactional data that contains detailed
information about items bought together by individuals. Even
after removing all personal characteristics of the buyer, which
can serve as links to his identity, the publication of such data is
still subject to privacy attacks from adversaries who have
partial knowledge about the set. Unlike previous works, we do
not distinguish data as sensitive and non-sensitive, but we
consider them both as potential quasi-identifiers and potential
sensitive data, depending on the point of view of the adversary.
We define a new version of the anonymity guarantee using
concept learning. Our anonymization model relies on
generalization using concept hierarchy and concept learning.
The proposed algorithms are experimentally evaluated using
real world datasets.